A variable neighborhood search and simulated annealing hybrid for the profile minimization problem

2017 ◽  
Vol 87 ◽  
pp. 83-97 ◽  
Author(s):  
Gintaras Palubeckis
Keyword(s):  
Simulated Annealing ◽  
Minimization Problem ◽  
Variable Neighborhood Search ◽  
Neighborhood Search

scholarly journals Pengembangan Algoritma Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS) untuk penyelesaian kasus Vehicle Routing Problem with Time Windows (VRPTW)

2017 ◽  
Vol 6 (1) ◽  
pp. 49
Author(s):  
Titi Iswari
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Variable Neighborhood Search ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Heuristic Method ◽  
Neighborhood Search ◽  
Routing Problem ◽  
Time Required

<p><em>Determining the vehicle routing is one of the important components in existing logistics systems. It is because the vehicle route problem has some effect on transportation costs and time required in the logistics system. In determining the vehicle routes, there are some restrictions faced, such as the maximum capacity of the vehicle and a time limit in which depot or customer has a limited or spesific opening hours (time windows). This problem referred to Vehicle Routing Problem with Time Windows (VRPTW). To solve the VRPTW, this study developed a meta-heuristic method called Hybrid Restart Simulated Annealing with Variable Neighborhood Search (HRSA-VNS). HRSA-VNS algorithm is a modification of Simulated Annealing algorithm by adding a restart strategy and using the VNS algorithm scheme in the stage of finding neighborhood solutions (neighborhood search phase). Testing the performance of HRSA-VNS algorithm is done by comparing the results of the algorithm to the Best Known Solution (BKS) and the usual SA algorithm without modification. From the results obtained, it is known that the algorithm perform well enough in resolving the VRPTW case with the average differences are -2.0% with BKS from Solomon website, 1.83% with BKS from Alvarenga, and -2.2% with usual SA algorithm without any modifications.</em></p><p><em>Keywords : vehicle routing problem, time windows, simulated annealing, VNS, restart</em></p>

Minimizing Total Flow Time in a Flow Shop with Blocking Using a Hybrid Variable Neighborhood Search and Simulated Annealing Algorithm

2014 ◽  
Vol 631-632 ◽  
pp. 57-61 ◽  
Author(s):  
Zhan Peng Xie ◽  
Chao Yong Zhang ◽  
Xin Yu Shao ◽  
Yong Yin
Keyword(s):  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Variable Neighborhood Search ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Time ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Total Flow ◽  
Total Flow Time

In this paper, a hybrid methodology that incorporates a simulated annealing (SA) approach into the framework of variable neighborhood search (VNS) is proposed to solve the blocking flow shop scheduling problem with the total flow time minimization. The proposed hybrid algorithm adopts SA as the local search method in the third stage of VNS, and uses a perturbation mechanism consisting of three neighborhood operators in VNS to diversify the search. To enhance the intensification search, best-insert operator is adopted to generate the neighbors in SA. To evaluate the performance of the proposed hybrid algorithm, computational experiments and comparisons were conducted on the well-known Taillard’s benchmark problems. The computational results and comparisons validate the effectiveness of the proposed algorithm.

Resolution Methods

2020 ◽  
pp. 29-60
Keyword(s):  
Linear Programming ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Symmetry Breaking ◽  
Constraint Programming ◽  
Design Problem ◽  
Variable Neighborhood Search ◽  
Neighborhood Search ◽  
Performance Profiles ◽  
Portfolio Design

The aim of this chapter is to introduce the different notions of the techniques used to solve the portfolio design problem. These techniques can be divided into two exact (or complete) methods and approached (or incomplete) methods. In the first part, the authors provide the exact approaches, namely linear programming and constraint programming, as well as the techniques of symmetry breaking, the modeling notions, and the different solving algorithms. The second part concerns approached methods, namely Simulated Annealing, IDWalk, Tabu Search, GWW, and Variable Neighborhood Search, including the techniques of studying the performance profiles of a method.

scholarly journals A Comparative Analysis of Simulated Annealing and Variable Neighborhood Search in the ATCo Work-Shift Scheduling Problem

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 636 ◽  
Author(s):  
Faustino Tello ◽  
Antonio Jiménez-Martín ◽  
Alfonso Mateos ◽  
Pablo Lozano
Keyword(s):  
Simulated Annealing ◽  
Solution Structure ◽  
Variable Neighborhood Search ◽  
Fitness Function ◽  
Phase 1 ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Shift Scheduling ◽  
Work Shift ◽  
Starting Point

This paper deals with the air traffic controller (ATCo) work shift scheduling problem. This is a multi-objective optimization problem, as it involves identifying the best possible distribution of ATCo work and rest periods and positions, ATCo workload and control center changes in order to cover an airspace sector configuration, while, at the same time, complying with ATCo working conditions. We propose a three-phase problem-solving methodology based on the variable neighborhood search (VNS) to tackle this problem. The solution structure should resemble the previous template-based solution. Initial infeasible solutions are built using a template-based heuristic in Phase 1. Then, VNS is conducted in Phase 2 in order to arrive at a feasible solution. This constitutes the starting point of a new search process carried out in Phase 3 to derive an optimal solution based on a weighted sum fitness function. We analyzed the performance in the proposed methodology of VNS against simulated annealing, as well as the use of regular expressions compared with the implementation in the code to verify the feasibility of the analyzed solutions, taking into account four representative and complex instances of the problem corresponding to different airspace sectorings.

scholarly journals An Approach Integrating Simulated Annealing and Variable Neighborhood Search for the Bidirectional Loop Layout Problem

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
Gintaras Palubeckis
Keyword(s):  
Simulated Annealing ◽  
Variable Neighborhood Search ◽  
Harmony Search ◽  
Hybrid Approach ◽  
Neighborhood Search ◽  
Hybrid Technique ◽  
Layout Problem ◽  
Loop Layout ◽  
Problem Instances ◽  
Loop Layout Problem

In the bidirectional loop layout problem (BLLP), we are given a set of machines, a set of locations arranged in a loop configuration, and a flow cost matrix. The problem asks to assign machines to locations so as to minimize the sum of the products of the flow costs and distances between machines. The distance between two locations is calculated either in the clockwise or in the counterclockwise direction, whichever path is shorter. We propose a hybrid approach for the BLLP which combines the simulated annealing (SA) technique with the variable neighborhood search (VNS) method. The VNS algorithm uses an innovative local search technique which is based on a fast insertion neighborhood exploration procedure. The computational complexity of this procedure is commensurate with the size of the neighborhood, that is, it performs O(1) operations per move. Computational results are reported for BLLP instances with up to 300 machines. They show that the SA and VNS hybrid algorithm is superior to both SA and VNS used stand-alone. Additionally, we tested our algorithm on two sets of benchmark tool indexing problem instances. The results demonstrate that our hybrid technique outperforms the harmony search (HS) heuristic which is the state-of-the-art algorithm for this problem. In particular, for the 4 Anjos instances and 4 sko instances, new best solutions were found. The proposed algorithm provided better average solutions than HS for all 24 Anjos and sko instances. It has shown robust performance on these benchmarks. For 20 instances, the best known solution was obtained in more than 50% of the runs. The average time per run was below 10 s. The source code implementing our algorithm is made publicly available as a benchmark for future comparisons.

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